Learning Cross-Lingual IR from an English Retriever

Yulong Li, Martin Franz, Md Arafat Sultan, Bhavani Iyer, Young-Suk Lee, Avirup Sil


Abstract
We present DR.DECR (Dense Retrieval with Distillation-Enhanced Cross-Lingual Representation), a new cross-lingual information retrieval (CLIR) system trained using multi-stage knowledge distillation (KD). The teacher of DR.DECR relies on a highly effective but computationally expensive two-stage inference process consisting of query translation and monolingual IR, while the student, DR.DECR, executes a single CLIR step. We teach DR.DECR powerful multilingual representations as well as CLIR by optimizing two corresponding KD objectives. Learning useful representations of non-English text from an English-only retriever is accomplished through a cross-lingual token alignment algorithm that relies on the representation capabilities of the underlying multilingual encoders. In both in-domain and zero-shot out-of-domain evaluation, DR.DECR demonstrates far superior accuracy over direct fine-tuning with labeled CLIR data. It is also the best single-model retriever on the XOR-TyDi benchmark at the time of this writing.
Anthology ID:
2022.naacl-main.329
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4428–4436
Language:
URL:
https://aclanthology.org/2022.naacl-main.329
DOI:
10.18653/v1/2022.naacl-main.329
Bibkey:
Cite (ACL):
Yulong Li, Martin Franz, Md Arafat Sultan, Bhavani Iyer, Young-Suk Lee, and Avirup Sil. 2022. Learning Cross-Lingual IR from an English Retriever. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4428–4436, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
Learning Cross-Lingual IR from an English Retriever (Li et al., NAACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.naacl-main.329.pdf
Code
 primeqa/primeqa
Data
MKQANatural Questions